Beyond Linear Growth: Adaptive Human Intelligence, Emotional Dynamics, and Willingness to Write in Artificial Intelligence-Mediated Informal Digital Learning of English (AI-IDLE)

The development of generative artificial intelligence (GenAI) is expanding AI-mediated informal digital learning of English (AI-IDLE) from predominantly receptive and conversational activities to cognitively demanding productive tasks such as academic writing. Yet little is known about how learners’ motivation and technology-related emotions develop over time, or how sustained interaction with GenAI may engage human capacities for evaluation and adaptation. Grounded in Complex Dynamic Systems Theory (CDST), this study investigated the longitudinal development of willingness to write (WTW), technology-related enjoyment (ENY), and technology-related anxiety (ANX) among Chinese undergraduates learning English as a Foreign Language (EFL) in teacher-supported AI-IDLE embedded within an 11-week English for Academic Purposes writing course. Twenty-six participants with complete records across 11 measurement occasions were included in the longitudinal analysis. Trajectory analysis, change-point analysis, and exploratory moving-window correlation analysis were combined with thematic analysis of learners’ reflections. WTW followed a nonlinear trajectory, declining during the middle period before a potential late-stage transition and subsequent increase, whereas ENY remained comparatively stable and ANX generally declined. The relationships of WTW with ENY and ANX also varied over time. Learners’ reflections revealed increasing recognition of human–AI complementarity and more selective, critical, and strategic engagement with GenAI. From a human-intelligence perspective, these patterns illustrate adaptive and metacognitive capacities involved in evaluating AI-generated information, adjusting strategies, and coordinating human and artificial resources. Rather than treating WTW or emotions as measures of intelligence, the findings position AI-IDLE as a context in which such human intellectual capacities may be exercised and expressed.

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Publication Details

Journal
Journal of Intelligence
Published
2026-10-13
DOI
https://doi.org/10.3390/jintelligence14100259
Primary Topic
EFL/ESL Teaching and Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

Beyond Linear Growth: Adaptive Human Intelligence, Emotional Dynamics, and Willingness to Write in Artificial Intelligence-Mediated Informal Digital Learning of English (AI-IDLE)

Yunyan Zhang, Chenghao Wang, Bin Zou, Lanfang Sun
Journal of Intelligence
EFL/ESL Teaching and Learning
article

Beyond Linear Growth: Adaptive Human Intelligence, Emotional Dynamics, and Willingness to Write in Artificial Intelligence-Mediated Informal Digital Learning of English (AI-IDLE)

Yunyan Zhang, Chenghao Wang, Bin Zou, Lanfang Sun
article en

Abstract

The development of generative artificial intelligence (GenAI) is expanding AI-mediated informal digital learning of English (AI-IDLE) from predominantly receptive and conversational activities to cognitively demanding productive tasks such as academic writing. Yet little is known about how learners’ motivation and technology-related emotions develop over time, or how sustained interaction with GenAI may engage human capacities for evaluation and adaptation. Grounded in Complex Dynamic Systems Theory (CDST), this study investigated the longitudinal development of willingness to write (WTW), technology-related enjoyment (ENY), and technology-related anxiety (ANX) among Chinese undergraduates learning English as a Foreign Language (EFL) in teacher-supported AI-IDLE embedded within an 11-week English for Academic Purposes writing course. Twenty-six participants with complete records across 11 measurement occasions were included in the longitudinal analysis. Trajectory analysis, change-point analysis, and exploratory moving-window correlation analysis were combined with thematic analysis of learners’ reflections. WTW followed a nonlinear trajectory, declining during the middle period before a potential late-stage transition and subsequent increase, whereas ENY remained comparatively stable and ANX generally declined. The relationships of WTW with ENY and ANX also varied over time. Learners’ reflections revealed increasing recognition of human–AI complementarity and more selective, critical, and strategic engagement with GenAI. From a human-intelligence perspective, these patterns illustrate adaptive and metacognitive capacities involved in evaluating AI-generated information, adjusting strategies, and coordinating human and artificial resources. Rather than treating WTW or emotions as measures of intelligence, the findings position AI-IDLE as a context in which such human intellectual capacities may be exercised and expressed.

Journal of IntelligenceVol. 14(10)
University of Liverpool (GB), Education University of Hong Kong (HK), Xi’an Jiaotong-Liverpool University (CN)
Xi’an Jiaotong-Liverpool University
Openalex Percentile: Top 3%
EFL/ESL Teaching and Learning
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